Software
AMD Demonstrates VQE on Instinct GPUs with Qiskit Aer
AMD showcases GPU-accelerated Variational Quantum Eigensolver (VQE) simulations using Qiskit Aer on Instinct GPUs, achieving chemical accuracy on the LiH molecule.
Image: AMD
AMD has demonstrated the use of GPU-accelerated simulators to run the Variational Quantum Eigensolver (VQE) algorithm on its Instinct GPU accelerators using Qiskit Aer. The demonstration focuses on simulating quantum circuits for the LiH molecule with varying basis sets, highlighting the potential of GPU acceleration in quantum chemistry simulations. The VQE algorithm, a hybrid quantum-classical approach, is used to approximate the ground state energy of molecules by minimizing the energy expectation value through iterative optimization. This method enables researchers to study quantum algorithms in a controlled environment, where exact solutions are known, and validate their performance on future quantum hardware. AMD's implementation leverages the ROCm platform to provide a scalable solution for quantum chemistry problems, emphasizing the importance of efficient simulation in advancing quantum computing applications. The results indicate that with the right configuration, VQE can achieve chemical accuracy on AMD hardware, offering a practical approach for exploring quantum algorithms. *Source: [amd](https://rocm.blogs.amd.com/artificial-intelligence/vqe-qiskit-aer/README.html)*
Key points
- AMD demonstrated GPU-accelerated VQE simulations using Qiskit Aer on Instinct GPUs.
- The demonstration focused on simulating the LiH molecule with increasing basis sets.
- VQE combines a parameterized quantum circuit with a classical optimizer to minimize energy expectation values.
- AMD's implementation leverages ROCm for scalable quantum chemistry simulations.
- The results indicate VQE can achieve chemical accuracy on AMD hardware with proper configuration.
- Qiskit Aer allows for exact emulation of quantum circuits, providing a controlled environment for algorithm validation.
- GPU acceleration improves performance compared to CPU simulations for quantum chemistry problems.